In a data center, there are hundreds of thousands storages, e.g. HDDs (Hard Disk Drive), SSDs (Solid State Drive), magnetic platters and even CDs (Compact Disk). The storages are connected to remote devices and can be accessed to provide a variety of services. The storages are the most costly components in the data center. On one hand, it needs to provision sufficient quantity of storages to fulfill requirements of workloads run over a portion of servers in the data center. On the other hand, due to long-term use, lifetimes of the storages are shorter than that used in personal computers. How to save the expense of storages in procurement and maintenance is a key factor to reduce fixed cost of the data center.
It is obvious that if a trend of operation of the data center is available, the storages can be automatically deployed for the most economical configuration. Then, the most effective use of the storages can be obtained, and accordingly, the target above can be achieved. However, due to unpredictable requirements from the workloads, all the storages are passively standby for use rather than premeditated provisioned. More spare storages are necessary and that incurs a burden which cannot be omitted. Under this situation, it is important to have a picture of lifetimes of the storages since the more accurate remaining lifetime of the storages that one can learn and handle, the less waste and the less risk it may cause. And in turn, more value can be provided.
Real lifetime of a storage is an objective data that can be obtained when the storage finally fails. No one can figure it out in the unknown future. It is lucky that there are some physical attributes of the storage that can be traced and recorded. With the associated records, by comparing among other storages in the same data center (working environment), the day one storage failed can be roughly predicted by some methods though the results are not always accurate. Now, a dilemma is faced by the administrator of the data center: to remove a storage from the data center too early before it actually fails, as is predicted to be failed soon by any predictive method, it is a cost waste; on the contrary, if too late, huge data will be lost without backups. It is not affordable for the data center.
Hence, reliable methods for determining terminating days for the storages are desired. There are some prior arts, such as U.S. Pat. No. 9,229,796 (system and method for determining disk failure indicator to predict future disk failures), U.S. Pat. No. 9,542,296 (disk replacement using a predictive statistical model), U.S. Pat. No. 9,612,896 (prediction of disk failure), U.S. Pat. No. 9,244,790 (system and method for predicting future disk failures), and US patent application No. 20170206026 (predicting solid state drive reliability), disclosing different methods. However, the results may not be so useful since the methods don't take dynamic changes of the environment into consideration. One accident may reduce or prolong lifetime of the storage. Therefore, a continuous assessment for remaining lifetime of storages in the data center is meaningful, expected, and required.